Formulating Regional Typologies

Dony Indiarto, Mohamad Nugraha

Agenda

  1. Understanding the Regional Typologies
  2. Methodology and Steps
  3. Case Studies and Examples
  4. Closing Remarks

What are Regional Typologies?

  • Definition
    • A categorization of geographical areas based on shared features.
    • Features often include quantifiable socio-economic indicators and environmental factors.

Why are Regional Typologies Important?

  • Regions vary in socio-economic and environmental aspects.
  • Typologies simplify this by creating manageable groups.
  • They facilitate comparisons and targeted management.

How Do Typologies and Indexes Differ?

Typology

Urban-rural typology for NUTS level 3 regions

Index

Global Food Security Index

How Do Typologies and Indexes Differ?

Criteria Regional Typologies Regional Indexes
What it is Categorises areas based on similar socio-economic, environmental, or institutional characteristics. A single, composite measure that quantifies the level of vulnerability to a threat.
Outcome Classifications like “urban area” or “rural area.” Numerical score ranking areas from low to high vulnerability.
Use-case Tailoring policies or interventions to different types of areas. Prioritising aid, resources, or interventions based on the ranking
Caveats Can oversimplify complexities; subject to analyst bias. May not capture the full situation; choice of indicators and weightings can be arbitrary.
Complementary aspects Provides rich, contextual information. Pinpoints areas needing immediate attention.

How Do We Make Regional Typologies?

  • Formulate Objectives
  • Identify Data requirement and availability
  • Data collection & pre-processing
  • Variable selection and PCA1
  • Cluster analysis2 and validation
  • Interpretation and comparisons

Case Studies

Case Study #1

Assessing and Characterizing Climate Vulnerabilities in Agriculture-Based Livelihoods: South Sulawesi

Aim: Analyze vulnerabilities of agriculture livelihoods at the provincial level; identify key risks, root causes, and adaptation strategies.

Approach: Define locations with similar biophysical and socio-economic characteristics as ‘typologies’ using PCA & K-means clustering.

Area of Interest: South Sulawesi province

Unit of Analysis: Sub-district (Kecamatan)

Case Study #1

Assessing and Characterizing Climate Vulnerabilities in Agriculture-Based Livelihoods: South Sulawesi

  • The second lowest household density after rural areas.

  • Limited accessibility to land-based livelihoods.

  • Economy predominantly relies on marine resources and maritime transport.

  • Inadequate health, education facilities, and market access.

  • Driest area in South Sulawesi with potential freshwater scarcity.

  • High drought indices with significant predicted rainfall decrease.

  • Over 40% of the population is underprivileged.

  • Mostly situated in warm lowlands; a 1°C temperature rise could be impactful.

  • Rural areas in South Sulawesi with the lowest household density.

  • Mountainous regions overlapping with protected areas, last bastions of unique Wallacean biodiversity.

  • High rainfall, low drought vulnerability (AI = 0.3).

  • In close proximity to forests, deforestation sites, and areas previously affected by fires.

  • Furthest from plantation concessions and limited road and river access.

  • Minimal changes in rainfall but the highest rate of temperature increase.

  • Highest erosion risk; lowest flood risk.

  • Almost 50% of the population falls in the bottom 40% economic bracket.

  • Closest to plantations, forests, and deforestation sites.

  • Furthest from previously burned areas in Sulawesi.

  • Medium household density per sub-district.

  • Moderate flood risk.

  • Moderate extent of irrigation.

  • Second highest risk of erosion and landslides after inland areas.

  • Highest rate of deforestation.

  • High risk of rainfall reduction, though not classified as arid.

  • About 1/3 of the population falls in the bottom 40% economic bracket.

  • Highest arable land coverage, with predominant irrigated areas.

  • Highest smallholder land ownership.

  • Second closest proximity to burned areas after urban regions.

  • Second highest access to roads and rivers following urban areas.

  • Second highest household density per district after urban regions; similar trend observed in health and education facilities.

  • Least proximity to forests due to limited forest cover.

  • Primarily located in lowlands; a temperature rise of 1.41°C significantly impacts this already warm region.

  • Expected to experience the most significant rainfall decrease (-84.31 mm) among typologies, making it the most drought-prone.

  • Flood risks during the rainy season, aggravated by minimal remaining forest cover.

  • High household density, with over a third belonging to the bottom 40% economic bracket.

  • Smallest area coverage (3%) with least arable land.

  • Lowest smallholder land ownership, following island regions.

  • Second closest proximity to burned areas after semi-urban regions.

  • Best access to roads and rivers.

  • Highest household density per district.

  • Easy access to health facilities, supermarkets, universities, and hospitals. However, each unit caters to a large number of households.

  • Only 15.1% of households fall within the bottom 40% economic bracket.

  • Warmest current temperatures compared to other regions; a rise of 1.43°C significantly impacts underprivileged local residents.

  • High flood risk, given many urban areas are near rivers.

  • Heavily reliant on food supplies from other regions.

Case Study #2

Village-level vulnerability classes of agricultural-based livelihoods to climate change in the studied districts in West Kalimantan Province

1 – Most Vulnerable: Located closest to oil palm plantations and factories, mining areas, and roads; has the largest shrub area per village; furthest from deforestation and has the lowest deforestation rate; has the smallest percentage of forested areas per village; high population.

2 – Highly Vulnerable: Located closest to burnt areas; has the largest percentage of oil palm area per village; situated nearer to oil palm concession areas and rubber factories; has the largest percentage of water bodies (lakes, rivers); has a lower deforestation rate but is located closer to deforestation areas; high population.

3 – Moderately Vulnerable: Located closest to rivers; has a larger percentage of oil palm area per village; situated slightly farther from oil palm companies and mining areas; has a larger percentage of forested and shrub areas per village; medium population.

4 – Less Vulnerable: Closest to deforestation areas and has the highest deforestation rate; located furthest from rivers; most distant from burnt areas; slightly closer to forested areas; slightly farther from oil palm concession areas; low population.

5 – Least Vulnerable: Has the largest percentage of forested areas per village; most remote; has the smallest percentage of shrub areas and oil palm areas per village; located closer to rivers and forested areas; has the lowest village population.

No Category Spatially explicit variables References
1 Distance to infrastructure Distance to oil palm plantation Landcover map 2017 from MoEF
2 Distance to infrastructure Distance to roads BIG
3 Distance to infrastructure Distance to a rubber factory ICRAF
4 Distance to infrastructure Distance to oil palm factory ICRAF
5 Distance to natural resources Distance to forest Landcover map 2017 from MoEF
6 Distance to natural resources Distance to river BIG
7 Distance to natural resources Distance to mining areas BIG
8 Distance to hazards area Distance to burnt areas KLHK 2015
9 Land use and land cover % area of oil palm per village Landcover map 2017 from MoEF
10 Land use and land cover % of forested areas per village Landcover map 2017 from MoEF
11 Land use and land cover % of shrubs areas per village Landcover map 2017 from MoEF
12 Land use and land cover % forested areas per district compared to district areas Landcover map 2017 from MoEF
13 Land use and land cover % oil palm areas at district level compare Landcover map 2017 from MoEF
14 Land use and land cover % water body compared to district areas Badan Informasi Geospatial (BIG), Geospatial Information Agency of Indonesia
15 Land use and land cover Distance to deforestation Landcover map 2017 from MoEF
16 Land use and land cover Deforestation area Landcover map 2017 from MoEF
17 Hazards Flood incidence Village Potentials, Statistics Indonesia 2018
18 Hazards Heavy flood incidence Village Potentials, Statistics Indonesia 2018
19 Hazards Fire incidences Village Potentials, Statistics Indonesia 2018
20 Hazards Drought incidences Village Potentials, Statistics Indonesia 2018
21 Demography Village population Village Potentials, Statistics Indonesia 2018
22 Distance to infrastructure Distance to oil palm concession areas Department of Estate Crops, West Kalimantan Province

Case Study #3

Peat Ecosystem Typologies in South Sumatra Province

Typology 1: High fire occurrences in protected areas, both with and without drainage. Lowest rates of deforestation, degradation, and plantation expansion. Low population and poverty, closest to cities with easy access.

Typology 2: High fire occurrences in cultivated areas, both with and without drainage. High deforestation rate, low degradation, and minimal plantation expansion. Highest population and poverty, near cities with easy access.

Typology 3: Fires occur in both cultivated and protected areas, irrespective of drainage. Highest rates of deforestation, degradation, and plantation expansion. Low population and poverty, furthest from cities with challenging access.

Typology 4: Most frequent fires in protected areas, regardless of drainage. Low deforestation, high degradation, and significant plantation expansion. Low population and poverty, far from cities with very limited access.

Typology 5: Most frequent fires in cultivated areas, irrespective of drainage. Low deforestation, high degradation, and significant plantation expansion. Low population and poverty, remote from cities with challenging access.

Classification of peat ecosystem revitalization actions in South Sumatra province
Typology R.3.1 Establishment of Peatland Stewardship Village R.3.2 Enhancing Livelihood Capacity R.3.3 Development of Alternative Commodities and Livelihood Sources
1 - Community awareness about the importance of the peat ecosystem, collaborating with district and provincial governments.

- Activities of the Peatland Stewardship Village aimed at increasing income and creating sources of livelihood for the community.
Conducted in synergy with district/city governments. Directed at non-agricultural livelihood sources (off-farm). Local industries that exist need support.
2 Peatland Stewardship Village is executed in an integrated manner in partnership with HTI companies and palm oil plantations. The goal is to enhance community access. Strengthening farmers’ livelihoods to protect the remaining peat ecosystems. Formulation of alternative livelihood sources. Livelihood sources oriented towards peat-friendly agriculture, mixed plantations combined with non-agricultural livelihood sources.
3 Peatland Stewardship Village is oriented towards fire prevention; conducted in an integrated manner in partnership with HTI companies and palm oil plantations. Strengthening farmers’ livelihoods through partnerships with plantation and HTI companies. Livelihoods aimed at forest and land fire prevention. Livelihood sources oriented towards peat-friendly agriculture and plantations combined with fisheries and livestock.
4 Peatland Stewardship Village geared towards fire prevention; enhancing community access to markets and high-economic value commodities. Strengthening farmers’ livelihoods through partnerships with plantation and HTI companies. Emphasis on preventing forest and land fires. Livelihood sources focused on peat-friendly agriculture and plantations combined with fisheries and livestock.
5 Peatland Stewardship Village focused on fire prevention; enhancing community access to markets and commodities of high economic value. Strengthening farmers’ livelihoods through partnerships with plantation and HTI companies. Emphasis on preventing forest and land fires. Livelihood sources focused on peat-friendly agriculture and plantations combined with fisheries and livestock.

Conclusion

  • We showcase examples on how regional typologies can be produced
  • The aim of the study shapes the data selection and interpretation of the typologies.
  • Iterative feedback from experts is essential for ensuring typologies align with objectives.

Footnotes

  1. Principle Component Analysis is a statistical technique used to simplify the complexity in data by highlighting its most important features.

  2. Cluster analysis is a machine learning procedure used to group similar items together based on their characteristics.